Using the Special Migration Statistics for research on human capital flows

Tony Champion

Paper presented to the ‘Census 2011: Impact and Potential’ conference, at the University of Manchester, 7-8 July, 2011

Acknowledgements: The paper draws on work carried out with Mike Coombes, Simon Raybould and Colin Wymer and funded by the Joseph Rowntree Foundation.

The 2001 Census SMS data was provided on CD by ONS and is Crown copyright.

Introduction to the SMS 2001Results on human capital flows

Looking forward to 2011

Introduction to the SMS 2001

Only one of several 2001 Census aggregate datasets containing migration data:

• Key Statistics and Univariate Statistics• Standard Tables • Census Area Statistics• Standard Theme Tables• Origin-Destination Statistics = SMS

But the SMS are the most valuable for migration analysis because of being for (1) flows between identified places; and (2) a number of migrants’ characteristics …

Tables in 2001 SMS

Variables (P Person; MGRP Moving Group Ref Person

Level 1 (district) Level 2 (ward)

Table Count Table Count

Age (P) MG101* 75 MG201* 51

Family status (P) MG102* 54

Ethnicity (P) MG103* 24 MG203* 9

Limiting illness (P) MG104** 84

Economic activity (P) MG105* 42

Moving groups in households MG106 16 MG202 4

Tenure (MGRP) MG107 32 MG205 8

Economic activity (MGRP) MG108* 336

NS-SeC (MGRP) MG109* 288 MG204 24

Gaelic/Welsh/Irish (P) MG110* 36Level 3 (output area): M301 Age* with 12 counts

* by gender, ** by age

Research on human capital flows for GB cities

Cities are increasingly being seen as drivers of regional economic growth, following a prolonged period of economic restructuring and population deconcentration

‘Urban renaissance’ policies (e.g. Urban Task Force, 1999) aiming for economic regeneration, but so far a very uneven impact (SOECR 2006; Champion and Townsend, 2011)

The ‘knowledge economy’ is regarded as vital to growth both nationally and sub-nationally, with the availability of high-quality human capital being a key factor (Florida, 2002)

Hence the importance of attracting and retaining people in more skilled occupations, and of the question: How well are Britain’s larger cities managing to do this?

Project for JRF using SMS MG109 data on groupings of NS-SeC of Moving Group Reference Person aggregated to best fit of 27 large cities …

The 27 cities

Focus on the Primary Urban Areas in grey

the rest of the city regions (RCRs) in white

NS-SEC groupings of MG109 categories

1.1 Large employers & higher managerial Higher M&P

1.2 Higher professional

2 Lower managerial & professional Lower M&P

3 Intermediate Intermediate

4 Small employers & own account workers

5 Lower supervisory & technical Low

6 Semi-routine

7 Routine

L15 Full-time students FT Students

L14.1 Never worked Other unclassifiedL14.2 Long term unemployed

L17 Not classifiable for other reasons

Within-UK migration, of MGRPs classified by NS-SeC at the census, for 27 cities combined

NS-SeC of MGRPs at Census Inflows Outflows In/out ratio

All MGRPs 389,902 399,368 0.976

Full Time Students 76,318 45,527 1.676

Other unclassified 31,331 45,472 0.689

All classified MGRPs 282,253 308,369 0.915

Higher M&P 67,162 69,515 0.966

Lower M&P 99,404 106,289 0.935

Intermediate 54,210 60,828 0.891

Low 61,477 71,739 0.857

Source: calculated from 2001 Census SMS Table MG109

In/out ratio of within-UK migration of MGRPs classified by NS-SeC at the census, for 27 cities combined

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

Low

Intermediate

Lower M&P

Higher M&P

All classified

Other unclassified

FT Student

All MGRPs

in/out ratio

More out than in

Unity = in/out balance

More in than out

Source: calculated from 2001 Census SMS Table MG109

Ditto, deviation from unity (i.e. from 1.0 = in/out balance):

In/out ratio for MGRPs, by broad NS-SeC, for the 27 Cities together

-0.4 -0.2 0.0 0.2 0.4 0.6 0.8

Higher M&P

Lower M&P

Intermediate

Lower occs

FT Student

Other

All MGRPs

deviation from unity

Source: calculated from 2001 Census SMS Table MG109

By 3 size groups:

In/out ratio for classified MGRPs, by broad NS-SeC type, for the 27 Cities grouped

-0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

27 cities

London

5 other large

21 others

deviation from unity

Higher M&P

Lower M&P

Intermediate

Lower occs

OUTFLOW GREATER THAN INFLOW INFLOW GREATER THAN OUTFLOW

Source: calculated from 2001 Census SMS Table MG109

In/out ratio for MGRPs, by broad NS-SeC type, for 27 Cities ranked by 'all classified'

-0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4

London

Brighton

Norwich

Bristol

Northampton

Reading

Edinburgh

Portsmouth

Derby

Plymouth

Bradford

Manchester

Preston

Southampton

Glasgow

Leeds

Newcastle

Nottingham

Middlesbrough

Leicester

Cardiff

Hull

Stoke

Birmingham

Liverpool

Sheffield

Coventry

deviation from unity

Higher M&P

Lower M&P

Intermediate

Lower skill

OUTFLOW GREATER THAN INFLOW INFLOW GREATER

Ditto, for 9 largest cities and just ‘longer-distance’ flowsPUAs of Nine Cities: in/out ratio for migration beyond the City Region,

2000-2001, for classified MGRPs by NS-SeC grouping at Census

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

London Birmingham Manchester Liverpool Newcastle Nottingham Sheffield Leeds Bristol

in/o

ut

rati

o (

>1

.0 =

ne

t g

ain

)

Higher M&P Lower M&P Intermediate Low skill

Source: calculated from 2001 Census SMS Table MG109

Classification of cities based on relative performance on longer-distance migration measures 2000-01 and

immigration rate 2001-2003

Type Cities

Gateway London Reading Brighton

StrongerBristol Derby Edinburgh Leeds Norwich Northampton Preston Portsmouth

ModerateBradford Glasgow Manchester Newcastle Nottingham Plymouth Sheffield Southampton

WeakerBirmingham Cardiff Coventry Hull Leicester Liverpool Middlesbrough Stoke

In/out ratios of flows between cities and the rest of their city regions by ‘longer-distance migration’ city types

City classes' in/out ratios with the rest of their city regions

0 0.2 0.4 0.6 0.8 1 1.2

Gateway

Stronger

Moderate

Weaker

in/out ratio

All MGRP

All Classified

Higher M&P

Source: calculated from 2001 Census SMS Table MG109

Balance

City types' Higher M&P in/out ratios for migration exchanges with London

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Other Gateway

Stronger

Moderate

Weaker

in/out ratio

Source: calculated from 2001 Census SMS Table MG109

Balance

Performance of the city classes on six labour market indicators

0.00 0.25 0.50 0.75 1.00 1.25 1.50

employment rate

employment rate change

school-leavers with5(+)GCSEs

graduate workforce

local job growth

commuting 10km(+)

relative to national average (England = 1.00)

Gateway

Stronger

Moderate

Weaker

Plus similar analyses for 27 other potential determinants:

demographic, cultural, socio-economic, housing, environmental

So, are Britain’s larger cities attracting/retaining more skilled people? Key findings:The 27-city aggregate picture for Higher M&Ps is quite encouraging:

‘neutral’ is better than previous Censuses have suggestedYet just 4 of 27 cities had Higher M&P in/out ratios of >1.0 (London,

Brighton, Derby, Northampton), with only 4 more coming close (Edinburgh, Reading, Manchester, Bristol)

Even so, as expected, stronger performance is associated with positive labour market factors (especially local job growth and a more graduate-intensive workforce)

There are also positive associations with some non-labour market factors, notably same-sex couples, people with no religion, high-status residents and White residents

Factors linked to more positive in/out ratios between cities and the rest of their regions echo those for their longer-distance migration, i.e. a ‘double whammy’ of net outflows for the weakest cities

Key feature is the dominance of London: very positive in/out ratio for Higher M&Ps overall and especially a strong net inflow of Higher M&Ps from other cities (presumably including recent graduates)

Relevance to policy and future research

These sorts of findings have generated much policy interest, with the results of this and similar studies being fed into both central and local government (e.g. DCLG, EMDA, MIER, TWCR)

Key policy questions include:• Can a more widespread urban renaissance be generated?• How can more cities attract/keep the Higher Managerial & Professional?

• Given the correlation results, can quality-of-life advantages counter

persistent regional imbalance in economic growth?• Are the weaker cities to face further housing-market failure because of

the double whammy of losing people to other parts of their own city regions as well as to other cities?

• Given that students moving to university boost most cities’ populations, what can be done to improve graduate retention in provincial cities?

Clearly, questions to which the Census can provide only partial answers, BUT the 2011 Census provides an important opportunity to see how ‘urban renaissance’ policies and other measures have altered the situation since the 2000-2001 migration year

Hopes for the 2011 Census in this context

Direct comparisons with the 2001 Census results will be impossible because of certain changes, mostly for the better (and NB: given the lack of comparability, plan for the best possible for 2011 output)

The ‘district’ geography of the SMS1 has altered, most notably with the creation of several unitary counties (could there be a more standardised geographical basis – or just use pre-2009 admin geography?)

The 2011 SMS data will not be subject to Small Cell Adjustment (good that a less damaging form of disclosure control will be used)

Migrants with ‘no usual address one year ago’ will have indicated their exact whereabouts (improvement on 0.5m just ticking a box in 2001)

Migrants whose usual address one year ago was for where they were a student in termtime will have ticked a box, distinguishing them from those already in work (improvement if this can be factored into output – and the best option given no question on economic-activity-1-year-ago )

One final improvement hoped for from the consultations for ODS output (starting later this year): additional* SMS1 and SMS2 tables on NS-SeC for all persons i.e. including non-RP members of Moving Groups and people in communal establishments (*or possibly substitute?)

Using the Special Migration Statistics for research on human

capital flows

Tony Champion

tony.champion@ncl.ac.uk